{"title":"广义量子 Arimoto-Blahut 算法及其在量子信息瓶颈中的应用","authors":"Masahito Hayashi and Geng Liu","doi":"10.1088/2058-9565/ad6eb1","DOIUrl":null,"url":null,"abstract":"Quantum information bottleneck was proposed by Grimsmo and Still (2016 Phys. Rev. A 94 012338) as a promising method for quantum supervised machine learning. To study this method, we generalize the quantum Arimoto–Blahut algorithm by Ramakrishnan et al (2021 IEEE Trans. Inf. Theory67 946) to a function defined over a set of density matrices with linear constraints so that our algorithm can be applied to optimizations of quantum operations. This algorithm has wider applicability, and we apply our algorithm to the quantum information bottleneck with three quantum systems. We numerically compare our obtained algorithm with the existing algorithm by Grimsmo and Still. Our numerical analysis shows that our algorithm is better than their algorithm.","PeriodicalId":20821,"journal":{"name":"Quantum Science and Technology","volume":"59 1","pages":""},"PeriodicalIF":5.6000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generalized quantum Arimoto–Blahut algorithm and its application to quantum information bottleneck\",\"authors\":\"Masahito Hayashi and Geng Liu\",\"doi\":\"10.1088/2058-9565/ad6eb1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Quantum information bottleneck was proposed by Grimsmo and Still (2016 Phys. Rev. A 94 012338) as a promising method for quantum supervised machine learning. To study this method, we generalize the quantum Arimoto–Blahut algorithm by Ramakrishnan et al (2021 IEEE Trans. Inf. Theory67 946) to a function defined over a set of density matrices with linear constraints so that our algorithm can be applied to optimizations of quantum operations. This algorithm has wider applicability, and we apply our algorithm to the quantum information bottleneck with three quantum systems. We numerically compare our obtained algorithm with the existing algorithm by Grimsmo and Still. Our numerical analysis shows that our algorithm is better than their algorithm.\",\"PeriodicalId\":20821,\"journal\":{\"name\":\"Quantum Science and Technology\",\"volume\":\"59 1\",\"pages\":\"\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quantum Science and Technology\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1088/2058-9565/ad6eb1\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quantum Science and Technology","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1088/2058-9565/ad6eb1","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
Generalized quantum Arimoto–Blahut algorithm and its application to quantum information bottleneck
Quantum information bottleneck was proposed by Grimsmo and Still (2016 Phys. Rev. A 94 012338) as a promising method for quantum supervised machine learning. To study this method, we generalize the quantum Arimoto–Blahut algorithm by Ramakrishnan et al (2021 IEEE Trans. Inf. Theory67 946) to a function defined over a set of density matrices with linear constraints so that our algorithm can be applied to optimizations of quantum operations. This algorithm has wider applicability, and we apply our algorithm to the quantum information bottleneck with three quantum systems. We numerically compare our obtained algorithm with the existing algorithm by Grimsmo and Still. Our numerical analysis shows that our algorithm is better than their algorithm.
期刊介绍:
Driven by advances in technology and experimental capability, the last decade has seen the emergence of quantum technology: a new praxis for controlling the quantum world. It is now possible to engineer complex, multi-component systems that merge the once distinct fields of quantum optics and condensed matter physics.
Quantum Science and Technology is a new multidisciplinary, electronic-only journal, devoted to publishing research of the highest quality and impact covering theoretical and experimental advances in the fundamental science and application of all quantum-enabled technologies.